Lecture 6 – Control¶

Data 94, Spring 2021¶

Motivating Example¶

def ready_to_graduate(year, units):
    return (year == 'senior') and (units >= 120)

ready_to_graduate('junior', 121)

False

ready_to_graduate('senior', 121)

True

def ready_greeting(year, units):
    is_ready = ready_to_graduate(year, units)
    if is_ready:
        print('ready to graduate!')
    else:
        print('not ready yet.')

ready_greeting('junior', 121)

not ready yet.

ready_greeting('senior', 121)

ready to graduate!

If-statements¶

The most basic if-statements only include an if.

def fancy_print(n):
    if n == 23:
        print('I love this number!')
    print(n)

fancy_print(5)

5

fancy_print(23)

I love this number!
23

def fancier_print(n):
    if n == 23:
        print('I love this number!')
    else:
        print('This is not my favorite number.')
    print(n)

fancier_print(5)

This is not my favorite number.
5

fancier_print(23)

I love this number!
23

You don't always need to use else.

def safe_divide(a, b):
    if b == 0:
        return 0
    return a / b

safe_divide(5, 0)

0

safe_divide(5, 2)

2.5

The above is equivalent to this; I personally prefer the above.

def safe_divide(a, b):
    if b == 0:
        return 0
    else:
        return a / b

Quick Check 1¶

def a_max(a, b, c):
    ...

Be concise!

def is_23(n):
    if n == 23:
        return True
    else:
        return False
    
def is_23(n):
    return n == 23

Elif¶

Let's build a grades calculator, according to this table:

Letter	Range
A	[90, 100]
B	[80, 90)
C	[70, 80)
D	[60, 70)
F	[0, 60)

Note, $[a, b)$ refers to the set of numbers that are greater than or equal to $a$, but less than $b$.

# if grade is between 90 and 100: return 'A'
# if grade is between 80 and 90: return 'B'
# if grade is between 70 and 80: return 'C'
# ...

# Takes in grade as number, computes letter grade, and prints 'Grade: letter'
def grade_converter(grade):
    if grade >= 90:
        return 'A'
    elif grade >= 80:
        return 'B'
    elif grade >= 70:
        return 'C'
    elif grade >= 70:
        return 'D'
    else:
        return 'F'

grade_converter(89)

'B'

grade_converter(70)

'C'

Here it's worth illustrating the difference between using elif and many ifs.

def grade_converter_print(grade):
    if grade >= 90:
        print('A')
    if grade >= 80:
        print('B')
    if grade >= 70:
        print('C')
    if grade >= 70:
        print('D')
    else:
        print('F')

grade_converter_print(89)

B
C
D

grade_converter_print(70)

C
D

The fact that we're using return and elif changes the behavior of our code dramatically. Watch out for this!

Quick Check 2¶

def num_pos(a, b):
    ...

Indentation¶

def indent_example(x):
    if x > 5:
        print('somewhat big!')
        if x % 2 == 0:
            print('and even too!')
    else:
        print('tiny.')

indent_example(15)

somewhat big!

indent_example(14)

somewhat big!
and even too!

indent_example(4)

tiny.

Truthy Values¶

bool(15)        # True

True

bool(None)      # False

False

def weird(x):
    if x:
        print('x is not 0')
    else:
        print('x is 0')

weird(5)

x is not 0

weird(0)

x is 0

def is_empty(s):
    return not s

is_empty('')

True

is_empty('zebra')

False

Demo¶

You should ignore all of the following code, except for the cell containing the definition of the function state_color.

import folium
from datascience import *

data = Table.read_table('data/states_elections.csv')
state_capitals = Table.read_table('data/us-state-capitals.csv')
data = data.join('State', state_capitals, 'name').select(['State', 'description', 'latitude', 'longitude', '2020']) \
    .relabeled(['State', 'description', '2020'], ['state', 'capital', 'federal vote'])

data

def make_label(r):
    return r[1] + ', ' + r[0]

data = data.with_columns('labels', data.apply(make_label))

Marker.map_table(data.select('latitude', 'longitude', 'labels'))

Now, don't ignore this cell!

def state_color(state, vote):
    if state == 'California':
        return 'green'
    elif vote == 'D':
        return 'blue'
    else:
        return 'red'

data = data.with_columns('colors', ['']*data.num_rows)
data = data.with_columns('colors', data.apply(lambda r: state_color(r[0], r[-3])))

Marker.map_table(data.select('latitude', 'longitude', 'labels', 'colors'))

state	capital	latitude	longitude	federal vote
Alabama	Montgomery	32.3777	-86.3006	R
Alaska	Juneau	58.3016	-134.42	R
Arizona	Phoenix	33.4481	-112.097	D
Arkansas	Little Rock	34.7466	-92.289	R
California	Sacramento	38.5767	-121.494	D
Colorado	Denver	39.7392	-104.985	D
Connecticut	Hartford	41.764	-72.6822	D
Delaware	Dover	39.1573	-75.5197	D
Florida	Tallahassee	30.4381	-84.2813	R
Georgia	Atlanta	33.749	-84.3882	D